We saw that 1) many metrics are stochastic, 2) what is stochastic can be hacked. This is the simplification of my work showing that “p-values are not p-values”, i.e. highly sample dependent, with a skewed distribution. For instance, for a “true” P value of .11, 53% of observations will show less than .05. This allows for hacking: in a few trials, a researcher can get a fake p-value of .01.
Paper is here and in Chapter 19 of SCOFT (Statistical Conseq of Fat Tails): Link to paper – A Short Note on P-Value Hacking